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Record W4398219281 · doi:10.24908/ohi.v2i1.17582

Deforestation Through Wildfire: One Health-Based Youth Storytelling to Facilitate Education and Awareness

2024· article· en· W4398219281 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueOne Health Innovation · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicYouth Development and Social Support
Canadian institutionsnot available
Fundersnot available
KeywordsStorytellingDeforestation (computer science)PsychologyEnvironmental planningGeographyEnvironmental scienceEnvironmental healthMedicineComputer scienceNarrative

Abstract

fetched live from OpenAlex

Deforestation by wildfires is a complex issue with numerous impacts on the health of humans, non-human animals, and the environment including resource depletion, habitat loss, and reduced biodiversity. While previous interventions have targeted the immediate downstream effects of wildfires, few actions involve upstream One Health approaches. This paper discusses a cost-effective action rooted in youth education as a preventative strategy for accidental wildfires. A story-based pamphlet was written to engage students from Grades 1 to 6 in Kingston, Ontario. To educate youth on the causes and effects of accidental wildfires using storytelling, this story was read to 27 children prior to engagement in grade-specific discussions. This action ultimately supports knowledge dissemination to foster awareness of wildfire prevention for future generations, improving outcomes for humans, non-human animals, and the environment.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.460
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.220
GPT teacher head0.406
Teacher spread0.185 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it